Womanium-Quantum-Drug-Discovery

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Womanium-Quantum-Drug-Discovery

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The project outlines using Bioinformatics, AI and Quantum Machine Learning to find Acetylcholinesterase (AChE) inhibitors as targets in Alzheimer’s disease (AD).

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# Quantum Drug Discovery for Alzheimer's Disease (QDD-AD)

## About

Welcome to the Quantum Drug Discovery for Alzheimer's Disease (QDD-AD) project, a part of the "Womanium Quantum Summer 2023 Program". Our goal is to harness the power of both classical and quantum computing to accelerate the process of drug discovery, focusing on Acetylcholinesterase (AChE) inhibitors for Alzheimer's disease.

This repository aims to educate and attract as many people as possible towards the promising field of Quantum Technologies and Quantum Machine Learning. Feel free to fork, play around, learn, and contribute back!

## Motivation

Alzheimer's Disease (AD) is the most common form of dementia, affecting millions of people worldwide. Traditional drug discovery methods are often time-consuming and costly. This project leverages Machine Learning, Bioinformatics, and especially Quantum Machine Learning to find AChE inhibitors efficiently, providing symptomatic relief for AD.

## What's Inside

- **Classical/ML model**: Contains a Random Forest ML model which runs on classical computers.
- **Quantum/ML model**: Contains Quantum Machine Learning algorithms which are designed to run on Quantum Processing Units (QPUs).
- **Dataset/Part 3**: Contains the preprocessed dataset used for training both classical and quantum models.

## Getting Started

1. **Fork this Repository**: Fork this repo to your GitHub account.

2. **Clone the Repo**: 
    ```bash
    git clone https://github.com/[Your Username]/[Repo Name].git
    ```

3. **Navigate to the Repo**: 
    ```bash
    cd [Repo Name]
    ```

4. **Install Dependencies**: 
    ```bash
    pip install -r requirements.txt
    ```

5. **Run the Code**: Follow the instructions in individual folders to run the classical and quantum models.

## How to Contribute

We welcome contributions from everyone. Here are some of the ways you can contribute:

- Improve or add new algorithms
- Optimize the code for better performance
- Add or improve documentation
- Report bugs and issues

Please read our [Contribution Guidelines](CONTRIBUTING.md) before submitting a pull request.

## References

- YouTube Tutorial: [Quantum Machine Learning](https://www.youtube.com/watch?v=jBlTQjcKuaY&t=3807s)

## License

This project is under the MIT License - see the [LICENSE.md](LICENSE.md) file for details.

<h2>References</h2>
https://www.youtube.com/watch?v=jBlTQjcKuaY&t=3807s

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